Who and how to choose combination therapy for inflammatory bowel disease: a comprehensive expert review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Therapeutic options in inflammatory bowel disease (IBD) have expanded significantly. Patients often experience primary or secondary loss of response to biologics or small molecules therapy. Determining which patients may benefit from combination of two therapies remains a key question. RECENT FINDINGS: Combination therapy leverages complementary mechanisms of action, conventionally using tumor necrosis factor antagonists simultaneously with immunosuppressive agents, and more recently using two advanced therapies together. Combination of two advanced therapies has shown promise in two recent randomized trials for improving clinical and endoscopic outcomes while maintaining acceptable safety profiles. Observational studies highlight its potential for refractory disease and complex phenotypes. Guidelines still conservatively recommend monotherapy for IBD patients, even for those at high risk for complications. SUMMARY: Advanced combination therapy (ACT) represents a potential significant advancement in managing IBD, offering treatment options for refractory cases, concomitant immune-mediated diseases and high-risk populations. Nonetheless, further randomized trials and registry data are needed to generate evidence to support broader adoption of this approach. Future research should focus on cost-effectiveness, longer-term treatment strategies and safety to refine its application in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".